{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "60af8f7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总图片数量: 400\n",
      "前10个文件名: ['10_0.jpg', '10_1.jpg', '10_2.jpg', '10_3.jpg', '10_4.jpg', '10_5.jpg', '10_6.jpg', '10_7.jpg', '10_8.jpg', '10_9.jpg']\n",
      "原始图片信息: <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x18B1BA52590>\n",
      "图片模式: RGB, 图片尺寸: (64, 64)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "正在提取特征...\n",
      "已处理 0/400 张图片\n",
      "已处理 50/400 张图片\n",
      "已处理 100/400 张图片\n",
      "已处理 150/400 张图片\n",
      "已处理 200/400 张图片\n",
      "已处理 250/400 张图片\n",
      "已处理 300/400 张图片\n",
      "已处理 350/400 张图片\n",
      "成功处理 400 张图片\n",
      "特征矩阵形状: (400, 1024)\n",
      "原始目标变量形状: (400,)\n",
      "原始人员ID范围: 1 - 40\n",
      "总人数: 40\n",
      "编码后目标变量形状: (400,)\n",
      "编码后人员ID范围: 0 - 39\n",
      "类别数量: 40\n",
      "原始标签: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
      " 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40]\n",
      "编码后标签: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39]\n",
      "\n",
      "============================================================\n",
      "数据可视化\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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oS8fuyspK1P3mN7+Juq2trajb3d2Nur29vajb39+PunRMpnuh9NlPn+l0T1yr1aIu/XyT/s2Y7gXS9033nI1GI+omPRdvbGxE3Z07d6Lu7t27E329VKnxnP5WSMaB/6ABAAAAKMwBDQAAAEBhDmgAAAAACnNAAwAAAFCYAxoAAACAwhzQAAAAABTmgAYAAACgMAc0AAAAAIU5oAEAAAAorDIej8elPwQAAADA/zL/QQMAAABQmAMaAAAAgMIc0AAAAAAU5oAGAAAAoDAHNAAAAACFOaABAAAAKMwBDQAAAEBhDmgAAAAACnNAAwAAAFDYvwEb8ix31SThqAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练集形状: (320, 1024)\n",
      "测试集形状: (80, 1024)\n",
      "训练集人员分布: 最小8张, 最大8张, 平均8.0张\n",
      "测试集人员分布: 最小2张, 最大2张, 平均2.0张\n",
      "\n",
      "将使用以下10个模型:\n",
      "  - 逻辑回归\n",
      "  - K近邻\n",
      "  - 决策树\n",
      "  - 随机森林\n",
      "  - AdaBoost\n",
      "  - SVM线性\n",
      "  - 高斯朴素贝叶斯\n",
      "  - 线性判别分析\n",
      "  - XGBoost\n",
      "  - LightGBM\n",
      "\n",
      "============================================================\n",
      "各模型人脸识别性能评估\n",
      "============================================================\n",
      "\n",
      "=== 逻辑回归 ===\n",
      "训练中... 完成! 训练时间: 0.19秒\n",
      "准确率: 0.9875\n",
      "\n",
      "=== K近邻 ===\n",
      "训练中... 完成! 训练时间: 0.00秒\n",
      "准确率: 0.9750\n",
      "\n",
      "=== 决策树 ===\n",
      "训练中... 完成! 训练时间: 0.21秒\n",
      "准确率: 0.5750\n",
      "\n",
      "=== 随机森林 ===\n",
      "训练中... 完成! 训练时间: 0.07秒\n",
      "准确率: 0.9750\n",
      "\n",
      "=== AdaBoost ===\n",
      "训练中... 完成! 训练时间: 1.24秒\n",
      "准确率: 0.0625\n",
      "\n",
      "=== SVM线性 ===\n",
      "训练中... 完成! 训练时间: 0.13秒\n",
      "准确率: 0.9875\n",
      "\n",
      "=== 高斯朴素贝叶斯 ===\n",
      "训练中... 完成! 训练时间: 0.01秒\n",
      "准确率: 0.9625\n",
      "\n",
      "=== 线性判别分析 ===\n",
      "训练中... 完成! 训练时间: 0.08秒\n",
      "准确率: 0.9875\n",
      "\n",
      "=== XGBoost ===\n",
      "训练中... 完成! 训练时间: 3.15秒\n",
      "准确率: 0.8625\n",
      "\n",
      "=== LightGBM ===\n",
      "训练中... 完成! 训练时间: 1.93秒\n",
      "准确率: 0.8750\n",
      "\n",
      "============================================================\n",
      "模型性能比较汇总\n",
      "============================================================\n",
      "         模型     准确率   训练时间(秒)\n",
      "0      逻辑回归  0.9875  0.188048\n",
      "5     SVM线性  0.9875  0.126850\n",
      "7    线性判别分析  0.9875  0.076737\n",
      "1       K近邻  0.9750  0.004203\n",
      "3      随机森林  0.9750  0.070342\n",
      "6   高斯朴素贝叶斯  0.9625  0.008741\n",
      "9  LightGBM  0.8750  1.933620\n",
      "8   XGBoost  0.8625  3.145229\n",
      "2       决策树  0.5750  0.205488\n",
      "4  AdaBoost  0.0625  1.239335\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\AppData\\Local\\Temp\\ipykernel_69748\\2414377574.py:289: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax1.set_xticklabels(performance_df['模型'], rotation=45, ha='right')\n",
      "C:\\Users\\Lenovo\\AppData\\Local\\Temp\\ipykernel_69748\\2414377574.py:303: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax2.set_xticklabels(performance_df['模型'], rotation=45, ha='right')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "最佳模型: 逻辑回归\n",
      "最佳准确率: 0.9875\n",
      "训练时间: 0.19秒\n",
      "============================================================\n",
      "\n",
      "详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         2\n",
      "           1       1.00      1.00      1.00         2\n",
      "           2       1.00      1.00      1.00         2\n",
      "           3       1.00      1.00      1.00         2\n",
      "           4       1.00      1.00      1.00         2\n",
      "           5       1.00      1.00      1.00         2\n",
      "           6       1.00      1.00      1.00         2\n",
      "           7       1.00      1.00      1.00         2\n",
      "           8       1.00      1.00      1.00         2\n",
      "           9       1.00      1.00      1.00         2\n",
      "          10       1.00      1.00      1.00         2\n",
      "          11       1.00      1.00      1.00         2\n",
      "          12       1.00      1.00      1.00         2\n",
      "          13       1.00      1.00      1.00         2\n",
      "          14       1.00      1.00      1.00         2\n",
      "          15       1.00      1.00      1.00         2\n",
      "          16       1.00      1.00      1.00         2\n",
      "          17       1.00      1.00      1.00         2\n",
      "          18       1.00      1.00      1.00         2\n",
      "          19       1.00      1.00      1.00         2\n",
      "          20       1.00      1.00      1.00         2\n",
      "          21       1.00      1.00      1.00         2\n",
      "          22       1.00      1.00      1.00         2\n",
      "          23       1.00      1.00      1.00         2\n",
      "          24       1.00      1.00      1.00         2\n",
      "          25       1.00      1.00      1.00         2\n",
      "          26       1.00      1.00      1.00         2\n",
      "          27       1.00      0.50      0.67         2\n",
      "          28       1.00      1.00      1.00         2\n",
      "          29       1.00      1.00      1.00         2\n",
      "          30       1.00      1.00      1.00         2\n",
      "          31       1.00      1.00      1.00         2\n",
      "          32       1.00      1.00      1.00         2\n",
      "          33       0.67      1.00      0.80         2\n",
      "          34       1.00      1.00      1.00         2\n",
      "          35       1.00      1.00      1.00         2\n",
      "          36       1.00      1.00      1.00         2\n",
      "          37       1.00      1.00      1.00         2\n",
      "          38       1.00      1.00      1.00         2\n",
      "          39       1.00      1.00      1.00         2\n",
      "\n",
      "    accuracy                           0.99        80\n",
      "   macro avg       0.99      0.99      0.99        80\n",
      "weighted avg       0.99      0.99      0.99        80\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "预测结果可视化\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "性能总结与建议\n",
      "============================================================\n",
      "\n",
      "    🎯 最佳模型: 逻辑回归\n",
      "    📊 准确率: 0.9875\n",
      "    ⏱️  训练时间: 0.19秒\n",
      "    👥 识别人数: 40人\n",
      "    📷 总图片数: 400张\n",
      "    \n",
      "    💡 改进建议:\n",
      "    1. 使用PCA降维减少特征数量，提高训练速度\n",
      "    2. 尝试数据增强技术扩充训练集\n",
      "    3. 调整模型超参数优化性能\n",
      "    4. 考虑使用深度学习模型(如CNN)获得更好效果\n",
      "    \n",
      "\n",
      "数据处理完成!\n"
     ]
    }
   ],
   "source": [
    "# 设置中文字体支持\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 1.读取人脸照片数据\n",
    "import os\n",
    "folder_path = r\"D:\\贺之恒的python\\pppython\\python商业数据分析\\@Python大数据分析与机器学习商业案例实战\\第12章 数据降维之PCA主成分分析\\源代码汇总_PyCharm格式\\olivettifaces\"\n",
    "\n",
    "# 检查文件夹是否存在\n",
    "if not os.path.exists(folder_path):\n",
    "    print(f\"错误: 文件夹路径不存在: {folder_path}\")\n",
    "    # 尝试使用相对路径\n",
    "    folder_path = \"olivettifaces\"\n",
    "    if not os.path.exists(folder_path):\n",
    "        print(f\"相对路径也不存在: {folder_path}\")\n",
    "        exit()\n",
    "\n",
    "names = os.listdir(folder_path)\n",
    "print(f\"总图片数量: {len(names)}\")\n",
    "print(\"前10个文件名:\", names[0:10])\n",
    "\n",
    "# 显示第一张图片\n",
    "from PIL import Image\n",
    "try:\n",
    "    img0 = Image.open(os.path.join(folder_path, names[0]))\n",
    "    print(f\"原始图片信息: {img0}\")\n",
    "    print(f\"图片模式: {img0.mode}, 图片尺寸: {img0.size}\")\n",
    "    \n",
    "    # 显示图片\n",
    "    plt.figure(figsize=(4, 4))\n",
    "    plt.imshow(img0, cmap='gray')\n",
    "    plt.title(f'示例图片: {names[0]}')\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"读取图片失败: {e}\")\n",
    "\n",
    "# 2.人脸数据处理 - 特征变量提取\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def preprocess_image(img_path, target_size=(32, 32)):\n",
    "    \"\"\"预处理单张图片\"\"\"\n",
    "    try:\n",
    "        img = Image.open(img_path)\n",
    "        img = img.convert('L')  # 转换为灰度图\n",
    "        img = img.resize(target_size)  # 调整尺寸\n",
    "        arr = np.array(img)\n",
    "        return arr.reshape(1, -1).flatten().tolist()\n",
    "    except Exception as e:\n",
    "        print(f\"处理图片 {img_path} 时出错: {e}\")\n",
    "        return None\n",
    "\n",
    "# 提取所有图片的特征\n",
    "print(\"\\n正在提取特征...\")\n",
    "X = []\n",
    "valid_names = []\n",
    "\n",
    "for i, name in enumerate(names):\n",
    "    if i % 50 == 0:  # 每50张图片显示一次进度\n",
    "        print(f\"已处理 {i}/{len(names)} 张图片\")\n",
    "    \n",
    "    img_path = os.path.join(folder_path, name)\n",
    "    features = preprocess_image(img_path)\n",
    "    \n",
    "    if features is not None:\n",
    "        X.append(features)\n",
    "        valid_names.append(name)\n",
    "\n",
    "print(f\"成功处理 {len(X)} 张图片\")\n",
    "\n",
    "if len(X) == 0:\n",
    "    print(\"错误: 没有成功读取任何图片\")\n",
    "    exit()\n",
    "\n",
    "X = pd.DataFrame(X)\n",
    "print(f\"特征矩阵形状: {X.shape}\")\n",
    "\n",
    "# 3.人脸数据处理 - 目标变量提取\n",
    "y = []\n",
    "for name in valid_names:\n",
    "    try:\n",
    "        # 假设文件名格式为 \"1_1.jpg\" 或类似\n",
    "        person_id = int(name.split('_')[0])\n",
    "        y.append(person_id)\n",
    "    except:\n",
    "        # 如果解析失败，使用文件名哈希作为ID\n",
    "        person_id = hash(name) % 40  # 假设有40个人\n",
    "        y.append(person_id)\n",
    "        print(f\"警告: 无法从文件名 {name} 解析ID，使用哈希值: {person_id}\")\n",
    "\n",
    "y = np.array(y)\n",
    "print(f\"原始目标变量形状: {y.shape}\")\n",
    "print(f\"原始人员ID范围: {min(y)} - {max(y)}\")\n",
    "print(f\"总人数: {len(np.unique(y))}\")\n",
    "\n",
    "# 修复：将标签转换为从0开始的连续整数\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "y_encoded = label_encoder.fit_transform(y)\n",
    "\n",
    "print(f\"编码后目标变量形状: {y_encoded.shape}\")\n",
    "print(f\"编码后人员ID范围: {min(y_encoded)} - {max(y_encoded)}\")\n",
    "print(f\"类别数量: {len(np.unique(y_encoded))}\")\n",
    "\n",
    "# 显示原始标签和编码后标签的对应关系\n",
    "unique_original = np.unique(y)\n",
    "unique_encoded = np.unique(y_encoded)\n",
    "print(f\"原始标签: {unique_original}\")\n",
    "print(f\"编码后标签: {unique_encoded}\")\n",
    "\n",
    "# 4.数据可视化\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"数据可视化\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "# 显示不同人员的图片\n",
    "unique_persons = np.unique(y_encoded)\n",
    "n_persons_to_show = min(9, len(unique_persons))\n",
    "\n",
    "fig, axes = plt.subplots(3, 3, figsize=(12, 12))\n",
    "for i, person_id in enumerate(unique_persons[:n_persons_to_show]):\n",
    "    row = i // 3\n",
    "    col = i % 3\n",
    "    \n",
    "    # 找到该人员的第一个图片\n",
    "    person_indices = np.where(y_encoded == person_id)[0]\n",
    "    if len(person_indices) > 0:\n",
    "        img_idx = person_indices[0]\n",
    "        img_array = X.iloc[img_idx].values.reshape(32, 32)\n",
    "        axes[row, col].imshow(img_array, cmap='gray')\n",
    "        \n",
    "        # 显示原始标签和编码后标签\n",
    "        original_label = y[img_idx]\n",
    "        encoded_label = y_encoded[img_idx]\n",
    "        axes[row, col].set_title(f'人员 {original_label}(编码:{encoded_label})\\n({len(person_indices)}张图片)')\n",
    "        axes[row, col].axis('off')\n",
    "\n",
    "plt.suptitle('不同人员的人脸图片示例\\n(括号内为编码后标签)', fontsize=16, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 5.划分训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded)\n",
    "\n",
    "print(f\"\\n训练集形状: {X_train.shape}\")\n",
    "print(f\"测试集形状: {X_test.shape}\")\n",
    "\n",
    "# 统计训练集和测试集的人员分布\n",
    "train_counts = np.unique(y_train, return_counts=True)\n",
    "test_counts = np.unique(y_test, return_counts=True)\n",
    "print(f\"训练集人员分布: 最小{min(train_counts[1])}张, 最大{max(train_counts[1])}张, 平均{np.mean(train_counts[1]):.1f}张\")\n",
    "print(f\"测试集人员分布: 最小{min(test_counts[1])}张, 最大{max(test_counts[1])}张, 平均{np.mean(test_counts[1]):.1f}张\")\n",
    "\n",
    "# 6.导入快速且高效的分类模型\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import numpy as np\n",
    "\n",
    "# 检查是否安装了高级模型\n",
    "try:\n",
    "    from xgboost import XGBClassifier\n",
    "    XGB_AVAILABLE = True\n",
    "except ImportError:\n",
    "    print(\"XGBoost未安装，跳过XGBoost模型\")\n",
    "    XGB_AVAILABLE = False\n",
    "\n",
    "try:\n",
    "    from lightgbm import LGBMClassifier\n",
    "    LGBM_AVAILABLE = True\n",
    "except ImportError:\n",
    "    print(\"LightGBM未安装，跳过LightGBM模型\")\n",
    "    LGBM_AVAILABLE = False\n",
    "\n",
    "# 创建模型字典（修复XGBoost和LightGBM的标签问题）\n",
    "models = {\n",
    "    '逻辑回归': LogisticRegression(max_iter=500, random_state=42, C=1.0),\n",
    "    'K近邻': KNeighborsClassifier(n_neighbors=3),\n",
    "    '决策树': DecisionTreeClassifier(max_depth=10, random_state=42),\n",
    "    '随机森林': RandomForestClassifier(n_estimators=50, max_depth=10, random_state=42, n_jobs=-1),\n",
    "    'AdaBoost': AdaBoostClassifier(n_estimators=50, random_state=42),\n",
    "    'SVM线性': SVC(kernel='linear', probability=True, random_state=42, C=1.0),\n",
    "    '高斯朴素贝叶斯': GaussianNB(),\n",
    "    '线性判别分析': LinearDiscriminantAnalysis(),\n",
    "}\n",
    "\n",
    "# 添加可选的模型（修复标签编码问题）\n",
    "if XGB_AVAILABLE:\n",
    "    models['XGBoost'] = XGBClassifier(\n",
    "        n_estimators=50, \n",
    "        max_depth=6, \n",
    "        random_state=42, \n",
    "        n_jobs=-1,\n",
    "        eval_metric='mlogloss'  # 明确指定多分类评估指标\n",
    "    )\n",
    "\n",
    "if LGBM_AVAILABLE:\n",
    "    models['LightGBM'] = LGBMClassifier(\n",
    "        n_estimators=50, \n",
    "        max_depth=6, \n",
    "        random_state=42, \n",
    "        n_jobs=-1,\n",
    "        verbose=-1  # 减少输出\n",
    "    )\n",
    "\n",
    "print(f\"\\n将使用以下{len(models)}个模型:\")\n",
    "for model_name in models.keys():\n",
    "    print(f\"  - {model_name}\")\n",
    "\n",
    "# 存储结果\n",
    "results = {}\n",
    "\n",
    "# 7.训练和评估各个模型\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"各模型人脸识别性能评估\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "import time\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    print(f\"\\n=== {model_name} ===\")\n",
    "    \n",
    "    try:\n",
    "        # 模型训练（带时间统计）\n",
    "        start_time = time.time()\n",
    "        print(\"训练中...\", end=' ')\n",
    "        \n",
    "        # 特殊处理XGBoost和LightGBM\n",
    "        if model_name in ['XGBoost', 'LightGBM']:\n",
    "            # 确保使用编码后的标签\n",
    "            model.fit(X_train, y_train)\n",
    "        else:\n",
    "            model.fit(X_train, y_train)\n",
    "            \n",
    "        training_time = time.time() - start_time\n",
    "        print(f\"完成! 训练时间: {training_time:.2f}秒\")\n",
    "        \n",
    "        # 模型预测\n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "        # 评估指标\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        \n",
    "        print(f\"准确率: {accuracy:.4f}\")\n",
    "        \n",
    "        # 存储结果\n",
    "        results[model_name] = {\n",
    "            'model': model,\n",
    "            'accuracy': accuracy,\n",
    "            'y_pred': y_pred,\n",
    "            'training_time': training_time\n",
    "        }\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"训练失败: {e}\")\n",
    "        continue\n",
    "\n",
    "# 8.比较模型性能\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"模型性能比较汇总\")\n",
    "print(\"=\"*60)\n",
    "\n",
    "if results:\n",
    "    performance_df = pd.DataFrame({\n",
    "        '模型': list(results.keys()),\n",
    "        '准确率': [result['accuracy'] for result in results.values()],\n",
    "        '训练时间(秒)': [result['training_time'] for result in results.values()]\n",
    "    }).sort_values('准确率', ascending=False)\n",
    "    \n",
    "    print(performance_df)\n",
    "    \n",
    "    # 9.可视化模型性能比较\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
    "    \n",
    "    # 准确率比较\n",
    "    colors = plt.cm.viridis(np.linspace(0, 1, len(performance_df)))\n",
    "    bars1 = ax1.bar(performance_df['模型'], performance_df['准确率'], color=colors)\n",
    "    ax1.set_title('模型准确率比较', fontsize=14, fontweight='bold')\n",
    "    ax1.set_ylabel('准确率', fontsize=12)\n",
    "    ax1.set_xticklabels(performance_df['模型'], rotation=45, ha='right')\n",
    "    ax1.grid(True, alpha=0.3, axis='y')\n",
    "    ax1.set_ylim(0, 1.0)\n",
    "    \n",
    "    # 在柱状图上添加数值\n",
    "    for bar in bars1:\n",
    "        height = bar.get_height()\n",
    "        ax1.text(bar.get_x() + bar.get_width()/2., height + 0.01,\n",
    "                f'{height:.3f}', ha='center', va='bottom', fontweight='bold')\n",
    "    \n",
    "    # 训练时间比较\n",
    "    bars2 = ax2.bar(performance_df['模型'], performance_df['训练时间(秒)'], color=colors)\n",
    "    ax2.set_title('模型训练时间比较', fontsize=14, fontweight='bold')\n",
    "    ax2.set_ylabel('训练时间(秒)', fontsize=12)\n",
    "    ax2.set_xticklabels(performance_df['模型'], rotation=45, ha='right')\n",
    "    ax2.grid(True, alpha=0.3, axis='y')\n",
    "    \n",
    "    # 在柱状图上添加数值\n",
    "    for bar in bars2:\n",
    "        height = bar.get_height()\n",
    "        ax2.text(bar.get_x() + bar.get_width()/2., height + 0.01,\n",
    "                f'{height:.1f}s', ha='center', va='bottom', fontweight='bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # 10.选择最佳模型并进行详细分析\n",
    "    best_model_name = performance_df.iloc[0]['模型']\n",
    "    best_model = results[best_model_name]['model']\n",
    "    best_accuracy = performance_df.iloc[0]['准确率']\n",
    "    \n",
    "    print(f\"\\n\" + \"=\"*60)\n",
    "    print(f\"最佳模型: {best_model_name}\")\n",
    "    print(f\"最佳准确率: {best_accuracy:.4f}\")\n",
    "    print(f\"训练时间: {performance_df.iloc[0]['训练时间(秒)']:.2f}秒\")\n",
    "    print(\"=\"*60)\n",
    "    \n",
    "    # 使用最佳模型进行详细预测分析\n",
    "    best_y_pred = results[best_model_name]['y_pred']\n",
    "    \n",
    "    print(\"\\n详细分类报告:\")\n",
    "    print(classification_report(y_test, best_y_pred, zero_division=0))\n",
    "    \n",
    "    # 11.可视化混淆矩阵\n",
    "    cm = confusion_matrix(y_test, best_y_pred)\n",
    "    \n",
    "    plt.figure(figsize=(12, 10))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "    plt.title(f'{best_model_name} - 混淆矩阵\\n总准确率: {best_accuracy:.3f}', \n",
    "              fontsize=16, fontweight='bold')\n",
    "    plt.colorbar()\n",
    "    \n",
    "    # 设置刻度标签\n",
    "    unique_labels = np.unique(y_test)\n",
    "    tick_marks = np.arange(len(unique_labels))\n",
    "    \n",
    "    # 如果标签太多，只显示部分\n",
    "    if len(unique_labels) > 20:\n",
    "        plt.xticks(tick_marks[::5], unique_labels[::5], rotation=45)\n",
    "        plt.yticks(tick_marks[::5], unique_labels[::5])\n",
    "    else:\n",
    "        plt.xticks(tick_marks, unique_labels, rotation=45)\n",
    "        plt.yticks(tick_marks, unique_labels)\n",
    "    \n",
    "    plt.xlabel('预测标签(编码后)', fontsize=12)\n",
    "    plt.ylabel('真实标签(编码后)', fontsize=12)\n",
    "    \n",
    "    # 在图中添加数字（如果矩阵不太大）\n",
    "    if cm.shape[0] <= 20:\n",
    "        thresh = cm.max() / 2.\n",
    "        for i in range(cm.shape[0]):\n",
    "            for j in range(cm.shape[1]):\n",
    "                plt.text(j, i, format(cm[i, j], 'd'),\n",
    "                        horizontalalignment=\"center\",\n",
    "                        color=\"white\" if cm[i, j] > thresh else \"black\",\n",
    "                        fontsize=8)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # 12.预测结果可视化\n",
    "    print(\"\\n\" + \"=\"*60)\n",
    "    print(\"预测结果可视化\")\n",
    "    print(\"=\"*60)\n",
    "    \n",
    "    # 随机选择一些测试样本进行可视化\n",
    "    n_samples = min(12, len(X_test))\n",
    "    indices = np.random.choice(len(X_test), n_samples, replace=False)\n",
    "    \n",
    "    fig, axes = plt.subplots(3, 4, figsize=(15, 12))\n",
    "    for i, idx in enumerate(indices):\n",
    "        row = i // 4\n",
    "        col = i % 4\n",
    "        \n",
    "        # 显示图片\n",
    "        img_array = X_test.iloc[idx].values.reshape(32, 32)\n",
    "        axes[row, col].imshow(img_array, cmap='gray')\n",
    "        \n",
    "        # 设置标题（绿色表示正确，红色表示错误）\n",
    "        true_label = y_test[idx]\n",
    "        pred_label = best_y_pred[idx]\n",
    "        color = 'green' if true_label == pred_label else 'red'\n",
    "        \n",
    "        # 获取原始标签\n",
    "        true_original = label_encoder.inverse_transform([true_label])[0]\n",
    "        pred_original = label_encoder.inverse_transform([pred_label])[0] if true_label != pred_label else true_original\n",
    "        \n",
    "        axes[row, col].set_title(f'真实: {true_original}({true_label})\\n预测: {pred_original}({pred_label})', \n",
    "                                color=color, fontweight='bold')\n",
    "        axes[row, col].axis('off')\n",
    "    \n",
    "    plt.suptitle(f'{best_model_name} - 预测结果示例\\n(绿色:正确, 红色:错误)\\n括号内为编码后标签', \n",
    "                fontsize=16, fontweight='bold')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # 13.性能总结\n",
    "    print(f\"\\n{'='*60}\")\n",
    "    print(\"性能总结与建议\")\n",
    "    print(f\"{'='*60}\")\n",
    "    \n",
    "    print(f\"\"\"\n",
    "    🎯 最佳模型: {best_model_name}\n",
    "    📊 准确率: {best_accuracy:.4f}\n",
    "    ⏱️  训练时间: {performance_df.iloc[0]['训练时间(秒)']:.2f}秒\n",
    "    👥 识别人数: {len(np.unique(y))}人\n",
    "    📷 总图片数: {len(X)}张\n",
    "    \n",
    "    💡 改进建议:\n",
    "    1. 使用PCA降维减少特征数量，提高训练速度\n",
    "    2. 尝试数据增强技术扩充训练集\n",
    "    3. 调整模型超参数优化性能\n",
    "    4. 考虑使用深度学习模型(如CNN)获得更好效果\n",
    "    \"\"\")\n",
    "    \n",
    "else:\n",
    "    print(\"没有模型成功训练，请检查数据和模型配置\")\n",
    "\n",
    "print(\"\\n数据处理完成!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a07f9fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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